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This is the second paper by Dr. Luftig, explaining how to use Total Asset Utilization and a cost allocation scheme to understand the true profitability of your products and/or services.

Originally published in Measuring Business Excellence Number 2 Volume 3

Background

This article is intended as a continuation of the article appearing in Volume 2.5 which described the Total Asset Utilization (TAU) metric, the calculation of which is the first step in assessing the true profitability of a current Customer/Product portfolio. In this article, we will explore how TAU data may be employed to:

assess the true profitability of an existing portfolio (the mix of customers and products sold by a firm); and

portray how these data may be employed by Sales & Marketing to design a strategy by which profitability may be enhanced (Customer/Product Rationalization; or CPR).

In order to illustrate these methods, we will utilize a limited (but actual) data set for a manufacturing firm selling a mix of four products (four alloys of steel or aluminum, or four types of bread products, for example) to three customers, all produced on a production line at a single manufacturing facility. The data set has been limited in this way so as to illustrate the basic steps associated with the CPR process. In actual applications, the steps that follow may be employed in exactly the same fashion as detailed (but with admittedly more complex analyses and results). Table I illustrates the current state of the sample firm’s portfolio, based upon a year of data, which is typically aggregated and summarized monthly. For this reason, the sample size of the data sets illustrated contain n = 12 cases or sets of observations. For this Table, the data in each Customer x Product cell may be interpreted as follows:

$332.95

Average Monthly Profit/Unit, based on Activity-Based Costing Measures

$6.29

Standard Deviation of Monthly Profit/Unit

20%

Average Monthly Percentage of Product Mix for that Product, As Purchased by that Customer

2000

Average # Units Purchased Monthly

31.38

Average Monthly TAU Index for that Customer & Product

A few observations are in order related to these data, before reviewing the complete Table.

The average monthly profit/unit represented in each cell should correspond to the monthly net profit per unit sold to that customer, for that product, as calculated on the basis of Activity-Based Costing[1] , or at least Cost Allocation, principles.

The variability in the net profit/unit measures represented by the Standard Deviation value is reflective of fluctuations in invoice pricing, claims, returns, shipment costs, and other factors which vary from month to month. For purposes of this article, we will assume that these fluctuations are relatively uniform across all customers and products; an assumption which, in actual applications, would have to be tested. If this variability were not equivalent across customer and product categories, it would have to be taken into account in the subsequent analyses.

The Average Monthly TAU indices were calculated as described in the previous article[2].

Method

The complete data set utilized to provide a baseline for the CPR analysis appeared as follows.

Table I - Description of the Current Portfolio

Customer

1

2

3

Total % of Mix

Product

1

$332.95

$6.29

20%

810

31.37

$312.76

$11.99

30%

2431

31.38

$294.45

$1.23

15%

608

31.43

23.75

2

$279.98

$4.22

25%

1013

43.48

$247.98

$10.57

10%

809

43.81

$245.06

$1.91

55%

2227

43.92

25.00

3

$292.75

5.45

45%

1823

46.35

$283.32

$12.58

30%

2431

46.27

$265.21

$1.17

15%

608

46.55

30.00

4

$298.64

$5.61

10%

405

57.34

$296.29

$7.72

30%

2431

57.33

$290.21

$1.48

15%

608

57.05

21.25

Total % of Mix

25

50

25

100

As shown by the data in Table I, the average monthly profit generated from this portfolio is $4,640,378.60. The next step in the CPR process is to determine the major source of variability associated with the Average Monthly Profit/Unit generated by each product and customer in the current mix. Treating the monthly data as a random sequence of n = 12 observations, a Two-Way ANOVA[3] is conducted using SPSS for Windows. The results appear as shown in Table II.

Table II - Two Way ANOVA for Revenue Data As shown by this table, the majority of the variability in the model associated with Revenue is a function of the Product category (refer to the MS column, which represents variance). In fact, 66.94% of the variability in Revenue ( ω2 ) is directly associated with the Product sold[4]. Although there is a significant interaction between the Customer and Product sold, this effect is only 5.10% of the variability observed. It makes sense in this case, therefore, to concentrate our portfolio improvement (for profitability purposes) on the Products we have chosen to sell. Illustration I, below, provides a visual display of these data, showing why the analysis resulted in the Product category yielding the most promising opportunity.

Illustration I

The next step is to conduct a second Two-Way ANOVA, with the Average Monthly TAU indices as the dependent variable. Table III presents the results of this analysis.

Table III - Two Way ANOVA for TAU Data

As shown by the data in Table III, ‘Product’ is the only variable reflecting significant differences in monthly Average TAU indices. Illustration II, which follows, presents a visual depiction of these data.

Illustration II

The third step in the CPR process is to determine those components of the TAU model which serve as predictors for productivity (simply measured at this point in terms of the number of Units produced), within each Product group (‘Units’). These data would have been collected and aggregated during the period of study preparing for this analysis. As described in the previous article, it would not typically be expected that (a) there would be a significant and important statistical relationship between TAU and Units; and (b) the same regression model would be serviceable for predicting productivity across all product groups. Table IV reflects the results of this analysis.

Table IV

As shown by the Regression data, the models that would predict productivity as generated from the TAU database vary significantly. While Duty Cycle, Availability, and Efficiency (in that order) would be used to predict Units for Product 1, for example, only Availability and Efficiency (respectively) would be used in conjunction with Products 3 and 4. Further, Yield is a significant component only in conjunction with Product 2, reinforcing the observations related to Quality presented in the first article.

The next step is to utilize the individual regression models to predict the number of units that would be produced at the current TAU levels, and at the current Customer mix, if only that Product were to be manufactured. Utilizing the principles stated in the first article for these calculations, Table V presents the results of this exercise, including an estimate for potential monthly profit using current average invoice (Revenue) levels. The profitability of each Product has been generated for both the observed component mean values, and the M.O.E. (Moment of Excellence) values for each component within each Product. The result of the ANOVA over the TAU data allows us to use a single prediction model for all Customers within each Product category. Had this not been the case, differentiated models would have to be generated and deployed.

Finally, the Customer mix within each Product category was calculated from within, versus across, all Product groups from the historical data base. For example, the Customer mix for Product category 1 was calculated as 21% (810/3849), 63% (2431/3849), and 16% (608/3849), respectively.

Table V - Potential Portfolio Analysis

Product

Regression Model for Unit* Prediction

Calculations Based Upon Observed Mean Values

Calculations Based Upon Observed M.O.E. Values

Component Values

Predicted Number of Average Units

Estimated Monthly Profitability

Component Values

Predicted Number of Average Units

Estimated Monthly Profitability

1

Y’ =6044.705 +

5806.979(DC) +

3557.41(AV) +

1598.634(EFF)

DC= 0.7233

AV= 0.8567

EFF = 0.5008

14,093

$4,426,179.80

2959*332.95 +

8879*312.76 +

2255*294.45

DC= 0.8100

AV= 0.9000

EFF = 0.5500

14,829

$4,657,340.10

3114*332.95 +

9342*312.76 +

2373*294.45

2

Y’ =617.076 +

5785.372(DC) +

2942.494(AV) +

9081.071(YLD)

DC= 0.7042

AV= 0.7183

YLD = .9800

15,704

$3,994,689.90

3926*279.98 +

3141*247.98 +

8637*245.06

DC= 0.7500

AV= 0.7900

YLD = 1.000

16,362

$4,162,083.70

4091*279.98 +

3272*247.98 +

8999*245.06

3

Y’ =2767.541 +

8234.548(AV) +

8084.202(EFF)

AV= 0.8753

EFF = 0.9283

17,480

$4,977,422.50

6642*292.75 +

8740*283.36 +

2098*265.21

AV= 0.9100

EFF = 0.9800

18,184

$5,177,536.20

6910*292.75 +

9092*283.36 +

2182*265.21

4

Y’ =2817.641 +

8823.93(AV) +

8160.779(EFF)

AV= 0.9675

EFF = 0.7575

17,537

$5,181,769.50

2062*298.64 +

12379*296.29 +

3095*290.21

AV= 0.9900

EFF = 0.8200

18,245

$5,391,276.60

2146*298.64 +

12879*296.29 +

3220*290.21

* The measure of productivity used by the firm

Summary

The steps we have employed in illustrating a CPR analysis to this point are as follows:

The development of an data base for concurrent productivity and TAU analysis, with linkage to Sales, (Activity-Based) Cost, and Profitability data.

The databases were then analyzed with the use of multiple ANOVA assessments to determine (a) the significant contributors to Profitability of the products sold by Product Category and Customer; and (b) to determine whether Product Category or Customer yielded significant differences in TAU indices.

Using the results of the ANOVAs, regression models were generated to allow for the prediction of productivity within the appropriate Customer / Product categories.

The regression models were then employed to conduct a Potential Portfolio analysis (Table V).

The potential portfolio analysis reveals a number of interesting observations, which would not normally have appeared as intuitively obvious when simply inspecting the descriptive data in the previous tables.

A cursory review of Table I might lead one to assume that Product 1 represented the best chance for the company to increase its profitability. In fact, when the TAU analysis is employed, this category is revealed to be the third most profitable for the company, despite its apparent advantage in margin per ton. In terms of asset dollars generated (the total number of dollars in profit per unit which could be generated on an equalized time basis) Products 4 and 3 are superior. Their lower margin per unit is more than offset by the degree to which they are ‘friendlier’ to the production facility.

The current monthly profit for the existing mix is $4,640,378.60. If the same capacity were utilized to produce and sell only Product 4, without any change in current productivity levels as measured by the TAU index, the potential profitability for the same facility would be $5,181,769.60; or 11.67% more. If the productivity associated with Product 4 were to be raised to only the M.O.E. levels, the TAU index for the product category would shift from 0.59 to 0.71 (at little or no cost in capital investment). This change would represent an increase in monthly profitability to $5,391,276.60, or a 16% increase over current state. If the mix within this category could be shifted toward Customer 1, and away from Customer 3, the change in profit level would increase even more.

Of course, the author is not suggesting that in this (or any actual) case, 100% of all of Product 4 which the facility could produce could actually be sold to the existing Customers at the higher unit levels. The discussion in item 2., above, is intended for illustrative purposes only. What is true, however, is that a matrix of this type could be used in a horizontally integrated effort to increase sales in the more truly profitable Product / Customer categories; the stated definition of CPR. One could easily calculate the effect of a 10% shift away from Product 2, for example, if the sales were re-allocated to (again, for example) Customer 1, for Products 3 and 4. If this activity is undertaken, incidentally, it would be essential to make certain that the Key Performance Indicators (KPIs) used to reward or provide incentives to the Sales force were in concert with the desired goals. For example, some companies using this method have abandoned the old KPI of ‘Units Sold’ (without regard to which units were sold) in favor of a KPI associated with a measure of the ‘Richness of Product Mix Sold’. This KPI would take into account the asset dollars generated for the company, as compared to simply tracking potentially illusory margin dollars.

In many industries, a ‘feed the beast’ mentality exists which tells management that it is better to sell ‘anything’ as opposed to letting facilities sit idle. They believe this to be true even if it creates bottlenecks and increased costs. A cursory review of the Portfolio Analysis presented on Table V, specifically associated with Product 2, shows the fallacy of this belief and illustrates how difficult conditions may become in the absence of considering the implications and effects of TAU as related to a firm’s Customer/Product mix.

Finally, the following observations may also be advanced:

The model and process as depicted may also be employed for Process (as well as Customer and Product rationalization). In organizations with multiple production lines and facilities producing, or capable of producing, the same product, this procedure often can lead to increased profitability through flow path optimization.

The model as presented is a dynamic, not static tool. Firms with automated management information systems (e.g. SAP) should have little trouble updating the decision matrix at appropriate periods, as improvements occur within the production system or as prices change.

ABOUT THE AUTHOR

Dr. Jeffrey T. Luftig is a Senior Instructor in the Management Division of the College of Business at the University of Colorado, Boulder, Colorado. Dr. Luftig has also authored a number of textbooks and articles on the topics of Business Performance Improvement, the Quality Sciences, and Experimental Design.

[4] The statistic ‘omega-squared’ ( w2)allows for the estimate of the importance, rather than statistical significance, of each effect in the ANOVA model as a function of explained variability. The general formula for this statistic is: